The data set referenced in this script is generated from the American Community Survey (ACS) and the Washington Office of Superintendent of Public Instruction (OSPI). These data sets provide data at the person-level, with the ability to look at the different indicators by the six equity demographic groups of interest.
This data set was compiled from PUMS data.
Looking at the fields in the data set## [1] "Disability_cat" "Income_cat" "LEP_cat" "Older_cat"
## [5] "POC_cat" "Youth_cat" "Total"
## [1] "educational_attainment" "healthcare_coverage"
## [3] "median_household_income" "household_poverty"
## [5] "median_gross_rent" "crowding"
## [7] "SNAP" "internet_access"
## [9] "Kindergarten readiness" "tenure"
## [11] "rent_burden"
## [1] 2021 2022 2019 2018 2017 2016 2015 2014 2013 2012 2011
In this section we make sure that the data set makes sense.
## [1] "Region" "King" "Kitsap" "Pierce" "Snohomish"
## [1] "Disability_cat" "Income_cat" "LEP_cat" "Older_cat"
## [5] "POC_cat" "Youth_cat"
These fields will vary by indicator:
## [1] "median"
## [1] 2021 2016 2011
## [1] "N/A"
There are 5 geographies and 6 equity focus groups (each with 2 subgroups). There are 3 years in the data set and the indicator specific field has 1 attribute(s), which means there should be a total of 180 rows.
## [1] 170
There are some missing
data.
If we look at the data by year and geography, there should be 12 entries per year/geography.
2011 is missing some data for all
geographies.
If we look at the data by year and focus group, there should be 10 entries per year/focus group.
##
## Disability_cat Income_cat LEP_cat Older_cat POC_cat Youth_cat
## 2011 0 10 10 10 10 10
## 2016 10 10 10 10 10 10
## 2021 10 10 10 10 10 10
The disability category is missing all
data in 2011.
If we look at the data by year and focus sub-group, there should be 5 entries per year/focus sub-group.
##
## English proficient Household with older adult Household with youth
## 2011 5 5 5
## 2016 5 5 5
## 2021 5 5 5
##
## Household without older adult Household without youth
## 2011 5 5
## 2016 5 5
## 2021 5 5
##
## Limited English proficiency Low Income Non-Low Income Non-POC POC
## 2011 5 5 5 5 5
## 2016 5 5 5 5 5
## 2021 5 5 5 5 5
##
## With disability Without disability
## 2011 0 0
## 2016 5 5
## 2021 5 5
If we look at the data by year and indicator attribute, there should be 60 entries per year/indicator attribute.
##
## N/A
## 2011 50
## 2016 60
## 2021 60
To check for 0s and NULLs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 22853 65734 84481 83492 102747 166615
There are no 0s or nulls.
In this section we start to explore the data visually -
distribution by the different dimensions within the data set. These
plots are helpful to check for outliers and get a higher level
understanding of the data in one visual, before slicing the data by
geography and equity focus group in the following sections.
The following code will need to be adjusted to fit the fields specific
to the data indicator. For educational attainment, we focus on those
with a Bachelor’s degree or higher. The following code establishes the
data frame that the rest of the analysis uses. If there are fewer than 2
indicator attributes, this section can be skipped/commented
out, but the code will need to be adjusted throughout.
This section isn’t relevant for this specific indicator because there aren’t unique indicator attributes.
In this section we explore trends by different groups with MOEs. These charts help to show any missing data by geography, year, or focus group/subgroup.
In this section we further develop the draft visuals for communicating the results and supporting the narrative for the Equity Tracker webpages. These charts are slightly more refined by slicing the data by geography and equity focus group. The line charts don’t include MOEs, but they help make connections between the same groups over time.
The 5 geographies are all included in the facets by geography, but
they could be separated out to create 5 individual charts - one for each
geography.
The 6 equity focus groups are all included in the facets by
geography, but they could be separated out to create 6 individual charts
- one for each focus group.
Resource for
visual
The code to make this is type of visual is long - adjust to indicator as
needed (scale_x_continuous, labs, label,
etc).
This section needs to be edited. Keep the code chunks commented out for now as we draft and refine the visuals.
This section includes visuals that were determined to be less useful. We didn’t want to lose the work, but didn’t want to include it in the main workflow. Feel free to comment out if you don’t want to adjust the arguments to fit the indicator of interest.
There are five charts for the different geographies: Region and the 4
counties.
There are 5 charts for the different geographies: Region and the 4 counties.
There are 5 charts for the different geographies: Region and the 4
counties.
There are 6 charts for the different equity groups: POC, low-income, etc.
There are 6 charts for the different equity groups: POC, low-income,
etc.